Demo Data from EDBT 2026 / reviewers in the wild / expert
A. J. Goetze
dblp:134/4489
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 1971
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1Theory of computation · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
2 papers |
Information theory · 50% Mathematical optimization · 37% Coding theory · 13% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Mathematical optimization › sparse learning
feature selection |
0.0 | 1 | 1970 | A modified figure of merit for feature selection in pattern recognition (Corresp.) · IEEE Trans. Inf. Theory 1970 |
Information theory › information measures
mutual information |
0.0 | 1 | 1970 | A modified figure of merit for feature selection in pattern recognition (Corresp.) · IEEE Trans. Inf. Theory 1970 |
Information theory › neural coding
efficient coding |
0.0 | 1 | 1971 | Techniques for Efficient Encoding of Features in Pattern Recognition · IEEE Trans. Computers 1971 |
Coding theory
source coding |
0.0 | 1 | 1971 | Techniques for Efficient Encoding of Features in Pattern Recognition · IEEE Trans. Computers 1971 |
Methods — techniques the papers use, named apart from their topics
mutual information criterion · 0.0feature encoding · 0.0binary digit reduction · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1971 | Techniques for Efficient Encoding of Features in Pattern RecognitionabstractTwo techniques are described for efficient encoding of features in pattern recognition. It is shown that the number of binary digits needed to represent patterns can be greatly reduced using efficient feature encoding. Four-thousand utterances based on a 40-word vocabulary are used to evaluate the techniques. J. E. Shoenfelt, Charles C. Tappert, A. J. Goetze |
IEEE Trans. Computers | 3 |
| 1970 | A modified figure of merit for feature selection in pattern recognition (Corresp.)abstractA modification of the conventional mutual-information figure of merit for feature selection in pattern recognition is described. A weighting function is combined with the mutual-information function that results in greater recognition accuracy for a linear classifier. J. E. Paul Jr., A. J. Goetze, John Ben O'Neal Jr. |
IEEE Trans. Inf. Theory | 2 |